Can Qwen3.5 9B run on RTX 4000 Ada 20GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Qwen3.5 9B needs ~9.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.7 GB, 51.1 tok/s, Runs well

9.7 GB required20.0 GB available

49% VRAM used

Fit status

Runs well

Decode

51.1 tok/s

TTFT

3785 ms

Safe context

172K

Memory

9.7 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsQwen3.5 9B on RTX 4000 Ada 20GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 51.1 tok/s decode · 3.8s TTFT (warm) · 128 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	51.1 tok/s	2065 ms	172K
Coding	C	Runs well	51.1 tok/s	3785 ms	172K
Agentic Coding	C	Runs well	51.1 tok/s	5506 ms	172K
Reasoning	C	Runs well	51.1 tok/s	4473 ms	172K
RAG	C	Runs well	51.1 tok/s	6882 ms	172K

Quantization options

How Qwen3.5 9B (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C46
Q3_K_S	3	4.4 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run Qwen3.5 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "unsloth/Qwen3.5-9B-GGUF" \
 --hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 9B well

👁 NVIDIA
RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 133%.119.3 tok/s decode

Raises estimated decode speed by about 133%.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 147%.126 tok/s decode

Raises estimated decode speed by about 147%.

~$1,599 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 101%.102.8 tok/s decode

Raises estimated decode speed by about 101%.